Section: Research Program

Different research axes

The goals of the team are biological and methodological, the two being intrinsically linked. Any division into axes along one or the other aspect or a combination of both is thus somewhat artificial. Following the evaluation of the team at the end of 2017, four main axes were identified, with the last one being the more recently added one. This axis is specifically oriented towards health in general, human or animal. The first three axes are: genomics, metabolism and post-transcriptional regulation, and (c)evolution.

Notice that the division itself is based on the biological level (genomic, metabolic/regulatory, evolutionary) or main current Life Science purpose (health) rather than on the mathematical or computational methodology involved. Any choice has its part of arbitrariness. Through the one we made, we wished to emphasise the fact that the area of application of ERABLE is important for us. It does not mean that the mathematical and computational objectives are not equally important, but only that those are, most often, motivated by problems coming from or associated to the general Life Science goal. Notice that such arbitrariness also means that some Life Science topics will be artificially split into two different Axes. One example of this is genomics and the two main health areas currently addressed that are intrinsically inter-related for now.

Axis 1: Genomics

Intra and inter-cellular interactions involve molecular elements whose identification is crucial to understand what governs, and also what might enable to control such interactions. For the sake of clarity, the elements may be classified in two main classes, one corresponding to the elements that allow the interactions to happen by moving around or across the cells, and another that are the genomic regions where contact is established. Examples of the first are non coding RNAs, proteins, and mobile genetic elements such as (DNA) transposons, retro-transposons, insertion sequences, etc. Examples of the second are DNA/RNA/protein binding sites and targets. Furthermore, both types (effectors and targets) are subject to variation across individuals of a population, or even within a single (diploid) individual. Identification of these variations is yet another topic that we wish to cover. Variations are understood in the broad sense and cover single nucleotide polymorphisms (SNPs), copy-number variants (CNVs), repeats other than mobile elements, genomic rearrangements (deletions, duplications, insertions, inversions, translocations) and alternative splicings (ASs). All three classes of identification problems (effectors, targets, variations) may be put under the general umbrella of genomic functional annotation.

Axis 2: Metabolism and post-transcriptional regulation

As increasingly more data about the interaction of molecular elements (among which those described above) becomes available, these should then be modelled in a subsequent step in the form of networks. This raises two main classes of problems. The first is to accurately infer such networks. Assuming such a network, integrated or “simple”, has been inferred for a given organism or set of organisms, the second problem is then to develop the appropriate mathematical models and methods to extract further biological information from such networks.

The team has so far concentrated its efforts on two main aspects concerning such interactions: metabolism and post-transcriptional regulation by small RNAs. The more special niche we have been exploring in relation to metabolism concerns the fact that the latter may be seen as an organism's immediate window into its environment. Finely understanding how species communicate through those windows, or what impact they may have on each other through them is thus important when the ultimate goal is to be able to model communities of organisms, for understanding them and possibly, on a longer term, for control. While such communication has been explored in a number of papers, most do so at a too high level or only considered couples of interacting organisms, not larger communities. The idea of investigating consortia, and in the case of synthetic biology, of using them, has thus started being developed in the last decade only, and was motivated by the fact that such consortia may perform more complicated functions than could single populations, as well as be more robust to environmental fluctuations. Another originality of the work that the team has been doing in the last decade has also been to fully explore the combinatorial aspects of the structures used (graphs or directed hypergraphs) and of the associated algorithms. As concerns post-transcriptional regulation, the team has essentially been exploring the idea that small RNAs may have an important role in the dialog between different species.

Axis 3: (Co)Evolution

Understanding how species that live in a close relationship with others may (co)evolve requires understanding for how long symbiotic relationships are maintained or how they change through time. This may have deep implications in some cases also for understanding how to control such relationships, which may be a way of controlling the impact of symbionts on the host, or the impact of the host on the symbionts and on the environment (by acting on its symbiotic partner(s)). These relationships, also called symbiotic associations, have however not yet been very widely studied, at least not at a large scale.

One of the problems is getting the data, meaning the trees for hosts and symbionts but even prior to that, determining with which symbionts the present-day hosts are associated (or are “infected” by as may be the term used in some contexts) which is a big enterprise in itself. The other problem is measuring the stability of the association. This has generally been done by concomitantly studying the phylogenies of hosts and symbionts, that is by doing what is called a cophylogeny analysis, which itself is often realised by performing what is called a reconciliation of two phylogenetic trees (in theory, it could be more than two but this is a problem that has not yet been addressed by the team), one for the symbionts and one for the hosts with which the symbionts are associated. This consists in mapping one of the trees (usually, the symbiont tree) to the other. Cophylogeny inherits all the difficulties of phylogeny, among which the fact that it is not possible to check the result against the “truth” as this is now lost in the past. Cophylogeny however also brings new problems of its own which are to estimate the frequency of the different types of events that could lead to discrepant evolutionary histories, and to estimate the duration of the associations such events may create.

Axis 4: Human, animal and plant health

As indicated above, this is a recent axis in the team and concerns various applications to human and animal health. In some ways, it overlaps with the three previous axes as well as with Axis 5 on the methodological aspects, but since it gained more importance in the past few years, we decided to develop more these particular applications. Most of them started through collaborations with clinicians. Such applications are currently focused on three different topics: (i) Infectiology, (ii) Rare diseases, and (iii) Cancer.

Infectiology is the oldest one. It started by a collaboration with Arnaldo Zaha from the Federal University of Rio Grande do Sul in Brazil that focused on pathogenic bacteria living inside the respiratory tract of swines. Since our participation in the H2020 ITN MicroWine, we started interested in infections affecting plants this time, and more particularly vine plants. Rare Diseases on the other hand started by a collaboration with clinicians from the Centre de Recherche en Neurosciences of Lyon (CNRL) and is focused the Taybi-Linder Syndrome (TALS) and on abnormal splicing of U12 introns, while Cancer rests on a collaboration with the Centre Léon Bérard (CLB) and Centre de Recherche en Cancérologie of Lyon (CRCL) which is focused on Breast and Prostate carcinomas and Gynaecological carcinosarcomas.

The latter collaboration was initiated through a relationship between a member of ERABLE (Alain Viari) and Dr. Gilles Thomas who had been friends since many years. G. Thomas was one of the pioneers of Cancer Genomics in France. After his death in 2014, Alain Viari took the (part time) responsibility of his team at CLB and pursued the main projects he had started.

Within Inria and beyond, the first two applications (Infectiology and Rare Diseases) may be seen as unique because of their specific focus (resp. respiratory tract of swines / vine plants on one hand, and TALS on the other). In the first case, such uniqueness is also related to the fact that the work done involves a strong computational part but also experiments performed within ERABLE itself.